Evaluating Public Health Interventions: 7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely

Using Randomized Controlled Trials to Estimate the Effect of Community Interventions for Childhood Asthma

INTRODUCTION

The Centers for Disease Control and Prevention's Controlling Childhood Asthma and Reducing Emergencies initiative aims to prevent 500,000 emergency department (ED) visits and hospitalizations within 5 years among children with asthma through implementation of evidence-based interventions and policies. Methods are needed for calculating the anticipated effects of planned asthma programs and the estimated effects of existing asthma programs. We describe and illustrate a method of using results from randomized control trials (RCTs) to estimate changes in rates of adverse asthma events (AAEs) that result from expanding access to asthma interventions.

METHODS

We use counterfactual arguments to justify a formula for the expected number of AAEs prevented by a given intervention. This formula employs a current rate of AAEs, a measure of the increase in access to the intervention, and the rate ratio estimated in an RCT.

RESULTS

We justified a formula for estimating the effect of expanding access to asthma interventions. For example, if 20% of patients with asthma in a community with 20,540 annual asthma-related ED visits were offered asthma self-management education, ED visits would decrease by an estimated 1,643; and annual hospitalizations would decrease from 2,639 to 617.

CONCLUSION

Our method draws on the best available evidence from RCTs to estimate effects on rates of AAEs in the community of interest that result from expanding access to asthma interventions.

A penalization approach to random-effects meta-analysis

Systematic reviews and meta-analyses are principal tools to synthesize evidence from multiple independent sources in many research fields. The assessment of heterogeneity among collected studies is a critical step when performing a meta-analysis, given its influence on model selection and conclusions about treatment effects. A common-effect (CE) model is conventionally used when the studies are deemed homogeneous, while a random-effects (RE) model is used for heterogeneous studies. However, both models have limitations. For example, the CE model produces excessively conservative confidence intervals with low coverage probabilities when the collected studies have heterogeneous treatment effects. The RE model, on the other hand, assigns higher weights to small studies compared to the CE model. In the presence of small-study effects or publication bias, the over-weighted small studies from a RE model can lead to substantially biased overall treatment effect estimates. In addition, outlying studies may exaggerate between-study heterogeneity. This article introduces penalization methods as a compromise between the CE and RE models. The proposed methods are motivated by the penalized likelihood approach, which is widely used in the current literature to control model complexity and reduce variances of parameter estimates. We compare the existing and proposed methods with simulated data and several case studies to illustrate the benefits of the penalization methods.

Familial co-aggregation of attention-deficit/hyperactivity disorder and autoimmune diseases: a cohort study based on Swedish population-wide registers

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) has been associated with several autoimmune diseases (AD), both within individuals and across relatives, implying common underlying genetic or environmental factors in line with studies indicating that immunological mechanisms are key to brain development. To further elucidate the relationship between ADHD and autoimmunity we performed a population-wide familial co-aggregation study.

METHODS

We linked Swedish national registries, defined a birth cohort with their biological relatives and identified individuals diagnosed with ADHD and/or 13 ADs. The cohort included 5 178 225 individuals born between 1960 and 2010, of whom 118 927 (2.30%) had been diagnosed with ADHD. We then investigated the associations between ADHD and ADs within individuals and across relatives, with logistic regression and structural equation modelling.

RESULTS

Within individuals, ADHD was associated with a diagnosis of any of the 13 investigated ADs (adjusted odds ratio (OR) =1.34, 95% confidence interval (CI) = 1.30-1.38) as well as several specific ADs. Familial co-aggregation was observed. For example, ADHD was associated with any of the 13 ADs in mothers (OR = 1.29, 95% CI = 1.26-1.32), fathers (OR = 1.14, 95% CI = 1.11-1.18), full siblings (OR = 1.19, 95% CI = 1.15-1.22), aunts (OR = 1.12, 95% CI = 1.10-1.15), uncles (OR = 1.07, 95% CI = 1.05-1.10) and cousins (OR = 1.04, 95% CI = 1.03-1.06). Still, the absolute risks of AD among those with ADHD were low. The genetic correlation between ADHD and a diagnosis of any of the investigated ADs was 0.13 (95% CI = 0.09-0.17) and the environmental correlation was 0.02 (95% CI = -0.03-0.06).

CONCLUSIONS

We found that ADHD and ADs co-aggregate among biological relatives, indicating that the relationship between ADHD and autoimmune diseases may in part be explained by shared genetic risk factors. The patterns of familial co-aggregation of ADHD and ADs do not readily support a role of maternal immune activation in the aetiology of ADHD. The findings have implications for aetiological models of ADHD. However, screening for autoimmunity among individuals with ADHD is not warranted.

Modifiable Lifestyle Recommendations and Mortality in Denmark: A Cohort Study

INTRODUCTION

Modifiable lifestyle behaviors represent a central target for public health interventions. This study investigates the association between adherence to 4 modifiable lifestyle recommendations and all-cause, cancer, or cardiovascular disease mortality.

METHODS

Investigators used data from the Danish Diet, Cancer and Health cohort (1993-2013; N=54,276). Lifestyle recommendations included smoking (never smoking), diet (adherence to 6 national food-based dietary guidelines), alcohol consumption (≤7 units per week for women and ≤14 units per week for men), and physical activity (≥30 minutes per day of moderate-to-vigorous leisure-time physical activity). Pseudo-values were used to estimate the adjusted risk differences and 95% CIs for all-cause, cancer, or cardiovascular disease mortality. Data were analyzed in 2019-2020.

RESULTS

A total of 8,860 participants died during a median follow-up of 17.0 years. Adherence to all modifiable lifestyle recommendations was associated with an 18.46% (95% CI= -20.52%, -16.41%) lower absolute risk of all-cause mortality than no adherence. Never smokers had a 13.19% (95% CI= -13.95%, -12.44%) lower risk, those adhering to dietary guidelines (diet score ≥5) had a 7.52% (95% CI= -8.89%, -6.14%) lower risk, and those adhering to recommended levels of alcohol (2.11%, 95% CI= -2.75%, -1.48%) and physical activity (1.58%, 95% CI= -2.20%, -1.00%) had a lower risk than those who did not adhere. Stronger associations were observed in men than in women and in older than in middle-aged participants.

CONCLUSIONS

Findings suggest that adherence to modifiable lifestyle recommendations is associated with a lower risk of mortality from all causes, cancer, and cardiovascular disease, underlining the importance of supporting adherence to national guidelines for lifestyle recommendations.

The Interaction Continuum

A common reason given for assessing interaction is to evaluate “whether the effect is larger in one group versus another”. It has long been known that the answer to this question is scale dependent: the “effect” may be larger for one subgroup on the difference scale, but smaller on the ratio scale. In this article, we show that if the relative magnitude of effects across subgroups is of interest then there exists an “interaction continuum” that characterizes the nature of these relations. When both main effects are positive then the placement on the continuum depends on the relative magnitude of the probability of the outcome in the doubly exposed group. For high probabilities of the outcome in the doubly exposed group, the interaction may be positive-multiplicative positive-additive, the strongest form of positive interaction on the “interaction continuum”. As the probability of the outcome in the doubly exposed group goes down, the form of interaction descends through ranks, of what we will refer to as the following: positive-multiplicative positive-additive, no-multiplicative positive-additive, negative-multiplicative positive-additive, negative-multiplicative zero-additive, negative-multiplicative negative-additive, single pure interaction, single qualitative interaction, single-qualitative single-pure interaction, double qualitative interaction, perfect antagonism, inverted interaction. One can thus place a particular set of outcome probabilities into one of these eleven states on the interaction continuum. Analogous results are also given when both exposures are protective, or when one is protective and one causative. The “interaction continuum” can allow for inquiries as to relative effects sizes, while also acknowledging the scale dependence of the notion of interaction itself.

How do age and major risk factors for mortality interact over the life-course? Implications for health disparities research and public health policy

A critical question in life-course research is whether the relationship between a risk factor and mortality strengthens, weakens, or remains constant with age. The objective of this paper is to shed light on the importance of measurement scale in examining this question. Many studies address this question solely on the multiplicative (relative) scale and report that the hazard ratio of dying associated with a risk factor declines with age. A wide set of risk factors have been shown to conform to this pattern including those that are socioeconomic, behavioral, and physiological in nature. Drawing from well-known principles on interpreting statistical interactions, we show that evaluations on the additive (absolute) scale often lead to a different set of conclusions about how the association between a risk factor and mortality changes with age than interpretations on the multiplicative scale. We show that on the additive scale the excess death risks posed by key socio-demographic and behavioral risk factors increase with age. Studies have not generally recognized the additive interpretation, but it has relevancy for testing life-course theories and informing public health interventions. We discuss these implications and provide general guidance on choosing a scale. Data from the U.S. National Health Interview Survey are used to provide empirical support.

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Studies often conclude that the effect of demographic and behavioral risk factors on mortality weakens with age.

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We show that this conclusion is premature as studies often fail to interpret their findings on the additive scale.

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We show empirically that on the additive scale the excess death risks posed by key risk factors strengthens with age.

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The general pattern of increasing susceptibility by age on the additive scale has not been previously recognized.

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We argue that the pattern has critical implications for sociological theory and public health policy.

Risk related therapy in meta-analyses of critical care interventions: Bayesian meta-regression analysis

PURPOSE

The relationship between treatment efficacy and patient risk is explored in a series of meta-analyses from the critical care domain, focusing on mortality outcome.

METHODS

Systematic reviews of randomized controlled trials were identified by electronic search over the period 2002 to July 2018. A Bayesian meta-regression model was employed, using the risk difference metric to estimate the relationship between mortality difference and control arm risk, and estimate the mortality difference with and without adjusting for control arm risk.

RESULTS

Of 780 initially identified published systematic reviews, 113 had appropriate mortality data comprising 123 analysable groups. The 123 meta-analyses were pharmaceutical therapeutic (59.3%), non-pharmaceutical therapeutic (24.4%) and nutritional (16.3%), with a 25% overall average control arm mortality. In 25/123 (20%) analyses, meta-regression indicated significant baseline risk (Bayesian 95% credible intervals excluding zero). In all analyses, the relationship between risk-difference and control arm risk was negative indicating a positive treatment effect with increasing control arm risk. Adjusted estimates identified six studies with significant positive treatment effects, not evident until after adjustment for control arm risk.

CONCLUSION

Underlying risk-related therapy is apparent in meta-analyses of the critically-ill and identification is of importance to both the conduct and interpretation of these meta-analyses.

Comparison of Methods to Generalize Randomized Clinical Trial Results Without Individual-Level Data for the Target Population

Our study explored the application of methods to generalize randomized controlled trial results to a target population without individual-level data. We compared 4 methods using aggregate data for the target population to generalize results from the international trial, Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), to a target population of trial-eligible patients in the UK Clinical Practice Research Datalink (CPRD). The gold-standard method used individual data from both the trial and CPRD to predict probabilities of being sampled in the trial and to reweight trial participants to reflect CPRD patient characteristics. Methods 1 and 2 used weighting methods based on simulated individual data or the method of moments, respectively. Method 3 weighted the trial's subgroup-specific treatment effects to match the distribution of an effect modifier in CPRD. Method 4 calculated the expected absolute benefits in CPRD assuming homogeneous relative treatment effect. Methods based on aggregate data for the target population generally yielded results between the trial and gold-standard estimates. Methods 1 and 2 yielded estimates closest to the gold-standard estimates when continuous effect modifiers were represented as categorical variables. Although individual data or data on joint distributions remains the best approach to generalize trial results, these methods using aggregate data might be useful tools for timely assessment of randomized trial generalizability.